PSI - Issue 79

Gabriella Bolzon et al. / Procedia Structural Integrity 79 (2026) 105–108

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structural response to degradation processes can be low (Li, 2021). Thus, variations in the system response due to initial damage can be confused with experimental errors. 4. Discussion and conclusion Artificial intelligence methods can effectively support the safety assessment of existing dams from different perspectives, processing large amounts of monitoring data and improving the quality of measurements obtained from both conventional instruments and more recently developed vision-based techniques. Despite continuous advances in algorithmic performance, comparative studies in the dam sector have shown that no single self-learning approach consistently outperforms the others under all conditions (Salazar et al., 2015). The main challenges, however, are not purely algorithmic but physical: the measurable quantities in dams often exhibit low sensitivity to early degradation processes, which hinders the development of fully reliable automatic diagnostic tools capable of detecting damage at an incipient stage and assessing its implications for structural safety. Even when anomalies can be detected and the most affected variables identified, understanding the type and severity of the underlying process requires comparison with physics-based simulations, typically carried out within the non linear finite element framework. Such analyses are computationally demanding and need to be systematically performed for each specific structure in order to establish a sufficiently comprehensive knowledge base for interpretation. References Archana, R., Jeevaraj, P.S.E., 2024. Deep learning models for digital image processing: a review. Artificial Intelligence Review 57, 11. 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